In video processing motion blur often constitutes a problem, especially motion blur on fast moving objects. For example, for frame-rate up-conversion well-defined shapes, which are free from motion blur, are much easier to match across frames. This leads to better time-interpolated frames. Also for simulations of virtual cameras based on real images deblurred frames are important, as the original blur will typically not match the camera motion of the simulated virtual camera.
In S. Cho et al.: “Removing Non-Uniform Motion Blur from Images”, IEEE Int. Conf. on Computer Vision (ICCV) 2007, pp. 1-8, a method is presented for removing non-uniform motion blur from a set of blurred images. This method is parameterized by the number of possible motions within the image. Thus, prior to the removal of motion blur, the number of rigidly moving image regions must be known. Unfortunately, this is not reasonable in a real-world video deblurring scenario.
For cases where the image blur slowly varies across the image, e.g. when the observed scene motion is not fronto-parallel to the camera's image plane or the whole scene shows rigid motion with respect to the camera, a number of techniques are capable of obtaining accurate estimates of the slowly spatially varying Point Spread Function (PSF), even from just a single image. These techniques include the works of Q. Shan et al.: “High-quality Motion Deblurring from a Single Image”, ACM Transactions on Graphics (Proceedings of SIGGRAPH 2008), Vol. 27 (2008), pp. 73:1-73:10, O. Whyte et al.: “Non-uniform Deblurring for Shaken Images”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2010, pp. 491-498, M. Hirsch et al.: “Fast Removal of Non-uniform Camera Shake”, IEEE Int. Conf. on Computer Vision (ICCV) 2011, pp. 463-470, and L. Xu et al.: “Depth-Aware Motion Deblurring”, Int. Conf. on Computational Photography 2012, pp. 1-8. The latter shows the specificity that the estimation of the spatially varying PSF is driven by the knowledge about the scene's depth in stereo setups.
None of the approaches described in the above identified publications is capable of estimating the various and localized types of motion blur that can occur simultaneously when a camera with slow shutter speed captures events with fast motion, such as a sports event with players moving in a variety of directions with a fast speed.